GitHub Copilot for Beginners: How to Get Started With AI Pair Programming

GitHub Copilot is an AI pair programmer that helps you write code faster and more efficiently. Instead of just doing simple autocomplete on variable names, it actually tries to understand the context of your code and your intent.

It can:
- Suggest entire lines or blocks of code as you type
- Generate functions from natural language comments
- Help explain existing code so you can understand it faster
- Suggest shell commands
- Draft pull request descriptions
- Assist with debugging tasks

And importantly, it’s not limited to a single language. You can use GitHub Copilot with:
- Popular languages like Python, JavaScript, TypeScript, Java, C#, Go, and more
- Frameworks and libraries in those ecosystems
- Shell scripts and configuration files

All of this happens directly inside your editor and on GitHub.com, so you’re not constantly jumping between your IDE and a web browser to search for snippets or Stack Overflow answers.

In my experience, the magic of Copilot is less about the “wow” factor and more about the little everyday moments: writing boilerplate code, setting up validation logic, or drafting test cases. Those tasks get faster, and honestly, less mentally draining.

A lot of people initially assume Copilot is just a supercharged autocomplete. But it’s more like a full coding companion that supports you at different points in your workflow.

Some examples of what it can do:

• From natural language to code

• Explaining code you don’t understand

• Helping with debugging

• Working where you already work
• Visual Studio Code
• Visual Studio
• JetBrains IDEs (like IntelliJ, PyCharm, WebStorm, etc.)
• Neovim
• Xcode
• GitHub Mobile
• Terminals
• Directly on GitHub.com

This tight integration means you stay in your flow. There’s no need to copy code into a separate AI chat tool, then paste it back. Less context switching, more coding.

And just to keep expectations realistic: Copilot isn’t perfect, and it can be wrong. You still need to review and test its suggestions. Think of it as a very fast, occasionally overconfident junior pair programmer who needs your supervision.

GitHub currently offers several licensing tiers for Copilot, targeting different kinds of users:

1. GitHub Copilot Free (for individuals)
This is a free tier that lets you try Copilot and get a feel for how it fits your workflow.

2. GitHub Copilot Pro (for individuals)
A paid plan for solo developers and freelancers who want the full feature set. It unlocks more advanced capabilities across editors, terminals, and GitHub.com.

3. GitHub Copilot Business
A plan for teams and companies that need managed access, policy controls, and organization-level administration.

4. GitHub Copilot Enterprise
A higher-level plan for large organizations, with deeper integration and governance features.

The specifics of each plan (like exact features and pricing) can change over time, so it’s best to check the official GitHub Copilot pricing and feature comparison page for the latest details.

But from a beginner’s perspective, what really matters is:
- You can sign up and start using Copilot as an individual, and
- If your company pays for Copilot Business or Enterprise, you might already have access through your work account.

If you’re a student, teacher, or open source maintainer, you may not need to pay at all.

GitHub offers free access to Copilot Pro for:

• Students and educators via [GitHub Education] (through the GitHub Student Developer Pack and related education programs)
• Popular open source project maintainers, as part of GitHub’s support for the open source ecosystem

So if you fall into one of those categories, it’s absolutely worth taking a few minutes to check eligibility and activate your free license.

To be honest, if you’re learning to code, having Copilot available can feel like having a tutor sitting next to you—it won’t replace actually understanding concepts, but it can help you explore patterns, read more code, and get unstuck faster.

Once your license is active on your GitHub account, you’re ready for the next step: installing GitHub Copilot in your editor.

In Visual Studio Code, Copilot comes as two closely related extensions:

1. GitHub Copilot – provides inline code completions (the grey “ghost text” in your editor)
2. GitHub Copilot Chat – adds a conversational AI chat panel to help you with coding tasks, explanations, debugging, and more

Here’s how to install them:

1. Open Visual Studio Code.
2. Click the Extensions icon in the Activity Bar (usually on the left side), or press `Ctrl+Shift+X` / `Cmd+Shift+X`.
3. In the search bar, type "GitHub Copilot".
4. Click the GitHub Copilot extension from GitHub.
5. Click Install.

Installing the main Copilot extension typically also installs Copilot Chat as its companion extension. After installation, VS Code may prompt you to reload or restart to complete the setup.

Once the extensions are installed, you need to sign in with the GitHub account that has your Copilot license.

You have two main options:

• Automatic prompt

• Manual sign-in

After that, VS Code opens your browser where GitHub will ask you to grant permissions so that Copilot can function properly. Click Continue and then Authorize Visual Studio Code (or the relevant GitHub application) when prompted.

Once authorization is complete:

• Return to VS Code.
• You should see a Copilot icon near the top of the window, next to the command center.
• You’ll also see a Copilot icon at the bottom status bar.

Clicking the bottom icon may prompt you to choose which account to use (if you have multiple), and then show a status indicator. When that status says “Ready”, Copilot is successfully installed and active.

If you see those icons and the ready status, you’re good to go. You might not notice anything else immediately yet, but the next section will show you how the suggestions actually appear as you type.

For this example, imagine we’re using PyCharm, but the steps are basically the same for other JetBrains IDEs.

1. Open your JetBrains IDE (e.g., PyCharm, IntelliJ, WebStorm).
2. Go to Settings/Preferences and then Plugins, or click the Plugins option from the welcome screen.
3. Open the Marketplace tab.
4. Search for "GitHub Copilot".
5. Click Install.
6. When prompted, restart the IDE to activate the plugin.

After restarting, open a project. You should see:

• A Copilot icon on the right-hand side of your editor.
• On the left side, two icons:
• One with a GitHub Copilot + question mark (for a quick overview and welcome message)
• One with a chat icon (for Copilot Chat)

If those icons are visible, the plugin is installed correctly.

If you haven’t previously authorized JetBrains with your GitHub account, the IDE will prompt you to sign in.

You can also trigger this manually:

1. Click the Copilot icon at the bottom of the IDE.
2. Select Login to GitHub.
3. Click Copy and Open when prompted – this opens your browser.
4. In the browser, click Continue to approve the requested permissions.
5. Paste the provided code into the appropriate field (using `Ctrl+V` / `Cmd+V`).
6. Click Authorize GitHub Copilot plugin.

Return to the IDE and you should see a message confirming that GitHub Copilot has been successfully installed and authorized.

To enable Copilot Chat:

1. Click the Chat icon on the side.
2. Click the Authorize button when prompted.
3. Again, choose Copy and Open, paste the code in your browser, and click Authorize GitHub Copilot for JetBrains IDE.
4. Go back to your IDE.

You should now see a greeting from Copilot Chat, confirming everything is set up. At this point, you can start using both inline completions and chat-based assistance while coding.

1. Open VS Code and create a new file, for example:
`validate.py` (the name matters a bit, because it hints at what the file might do).

2. Start typing. For instance, write:
```python
import re
```

3. After you press Enter, GitHub Copilot may start generating suggestions based on the context. Because the file is named something like `validate.py`, Copilot might infer that you want to validate email addresses and suggest a function definition.

You’ll see this as grey “ghost text” appearing ahead of your cursor. That’s the inline suggestion.

• Press Tab to accept the suggestion.
• Press Esc to dismiss it.

If Copilot suggests a full email validation function, it might include:
- A regular expression for email validation
- A function body that checks if a given string matches that pattern
- Maybe even a simple `main` function to run the validation

Once you accept a suggestion and hit Enter, Copilot often continues by suggesting the next logical block of code. It feels a bit like it’s one step ahead, helping you scaffold the entire file.

Sometimes Copilot will show one inline suggestion, but there may be multiple possible completions behind the scenes.

If you hover over the suggestion or use your editor’s shortcut (for example, `Ctrl+Enter` in some setups), you can open the Completions panel. There you can:

• View multiple alternative suggestions for the same spot
• Compare different implementations
• Choose the one that best matches what you want

This is useful when you feel like, “Hmm, that’s not bad, but I wonder if there’s another way Copilot could write this.” Chances are, there is.

One important detail: GitHub Copilot is nondeterministic. That’s a fancy way of saying you won’t always get the exact same suggestion twice—even with identical code. If you run similar tests on your machine and see different suggestions than someone else, that’s completely normal.

So don’t worry if your `validate.py` ends up slightly different from another tutorial or demo; what matters is that the suggestion is correct, readable, and suits your needs.

Inline suggestions are just one part of the story. The Copilot Chat experience can really help you understand and refactor code more confidently.

Let’s say Copilot just generated an email validation function for you, and you’re seeing it for the first time. Maybe it uses a regular expression that looks a bit cryptic.

Here’s how you might use Copilot Chat:

1. Open the Copilot Chat panel in VS Code (for example, via the Copilot icon or the dedicated Chat view).
2. Type a prompt like:

`@copilot explain this file`
or simply

`Explain this code line by line`

(The exact syntax may vary, but the idea is to ask Copilot to explain the currently open file.

3. Copilot Chat will produce a formatted explanation of your code. Typically, it describes:
- What each function does
- How the regular expression works
- The flow of your main function

You’ll notice the chat assistant has visibility into your open file, so it can tailor its explanation to your actual code context.

Now imagine you want to improve your validation function. For example, you might want to:
- Allow users to enter multiple email addresses at once
- Make the regular expression more robust

Instead of rewriting everything manually, you can simply ask Copilot Chat something like:

> "Allow users to enter multiple email addresses for validation and also improve the regex to be more robust."

Copilot Chat typically responds with:
- A short plan (what it’s going to change)
- An updated code block that includes all the modifications
- A list of changes or rationale for what was updated

You’ll often see a button like “Apply in editor”:

• Click it to let Copilot automatically modify your file with the new code
• Review the changes directly in your editor

From there, you can run and test the updated script as usual.

This kind of workflow—generate, explain, refine—is where Copilot really starts to feel like a proper collaborator instead of a fancy autocomplete.

Of course, as with all AI-generated code, make sure you:
- Read through the changes
- Verify the logic
- Add or run tests

Copilot can get you 80–90% of the way there faster, but you’re still the final decision-maker.

While many generic AI tools can generate snippets of code, GitHub Copilot is specifically designed for programming and integrated deeply into the developer workflow.

Some key differences:

• Context awareness in your editor

• First-class integration with GitHub
• Draft and refine pull request descriptions
• Summarize code changes
• Assist with repository-level understanding

• Support across multiple IDEs and environments

In other words, GitHub Copilot isn’t just a generic chatbot that happens to know how to code—it’s a developer-focused AI tool that sits right next to your cursor.

One of the underrated benefits of GitHub Copilot is the reduction in context switching.

Without Copilot, a typical workflow might look like:
- Type some code
- Get stuck on a regex or API usage
- Switch to your browser
- Google for examples, or open Stack Overflow
- Copy-paste a snippet
- Adapt it to your context

With Copilot, the idea is that much of this happens in-place:
- You start typing the logic you want
- Copilot suggests something that’s already adapted to your file
- If you’re not sure what it does, you ask Copilot Chat to explain it
- If it’s slightly off, you ask Copilot to adjust or improve it

No extra tabs. No bouncing between screens every couple of minutes. That might sound small, but over a full day of work or learning, it adds up.

So when you hear “GitHub Copilot,” you can think of it as your personal AI pair programmer—one that lives in your editor and on GitHub.com, and is optimized for writing, understanding, and improving code.

Copilot responds to context. That includes:
- The file name
- Your existing code
- Your comments and docstrings

If your file is named `validate.py`, Copilot will likely propose validation-related logic. If your comment says `# parse CSV file and return a list of dictionaries`, it will usually generate a function that does precisely that.

So, don’t be shy about writing clear comments that describe what you want. In many cases, you can:
- Write a detailed comment describing your function
- Let Copilot propose the full implementation

Then you can tweak and refine instead of starting from a blank file.

Even though Copilot can feel eerily smart, you still need to apply your own judgment.

A few healthy habits:
- Read every suggestion before accepting it
- Test the generated code, especially for edge cases
- Refactor generated code to match your style and standards

To be honest, Copilot can occasionally propose something that looks correct but is subtly wrong or inefficient. That’s why your expertise—or your willingness to carefully check its output—is still essential.

Think of Copilot’s suggestions as fast first drafts instead of final answers.

Copilot Chat isn’t only for fixing bugs or rewriting code. It can be a learning tool as well.

You can ask it to:
- Explain unfamiliar syntax or APIs
- Compare two approaches (e.g., iterative vs. recursive)
- Suggest improvements for readability or performance

This is especially useful when working with code that you didn’t write originally—legacy codebases, open source contributions, or even code generated by Copilot itself.

Over time, this back-and-forth can help you:
- Build intuition for idiomatic patterns
- Understand how different libraries fit together
- Level up your skills while actually shipping things